mlsanityData to 14 Oct 2026

"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanations

arXiv 2211.14858 ↗First posted 27 Nov 2022cs.LG cs.AI cs.CYcited by 9

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Abstract (arXiv)

Counterfactual explanations are a popular type of explanation for making the outcomes of a decision making system transparent to the user. Counterfactual explanations tell the user what to do in order to change the outcome of the system in a desirable way. However, it was recently discovered that the recommendations of what to do can differ significantly in their complexity between protected groups of individuals. Providing more difficult recommendations of actions to one group leads to a disadvantage of this group compared to other groups. In this work we propose a model-agnostic method for computing counterfactual explanations that do not differ significantly in their complexity between protected groups.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 19 of its 39 references that are in the map. First posted on arXiv 27 Nov 2022.

Builds on

19 of 39 references.

References · 19

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  2. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  3. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  4. 2019FACE: Feasible and Actionable Counterfactual Explanationscited by 233
  5. 2019Interpretable Counterfactual Explanations Guided by Prototypescited by 223
  6. 2016Model-Agnostic Interpretability of Machine Learningcited by 219
  7. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
  8. 2021A survey on datasets for fairness-aware machine learningcited by 157
  9. 2021Counterfactual Explanations Can Be Manipulatedcited by 113
  10. 2019The relationship between trust in AI and trustworthy machine learning technologiescited by 78
  11. 2020On the Fairness of Causal Algorithmic Recoursecited by 70
  12. 2019Equalizing Recourse across Groupscited by 55
7 more
  1. 2021Evaluating Robustness of Counterfactual Explanationscited by 41
  2. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  3. 2019On the computation of counterfactual explanations -- A surveycited by 33
  4. 2020Convex Density Constraints for Computing Plausible Counterfactual Explanationscited by 32
  5. 2022On the Privacy Risks of Algorithmic Recoursecited by 32
  6. 2021A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanationscited by 19
  7. 2021Convex optimization for actionable & plausible counterfactual explanationscited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 0; 2026: 2.

9 papers.

  1. 2023Robustness Implies Fairness in Causal Algorithmic Recoursecited by 12
  2. 2024Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Changecited by 10
  3. 2024Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunitycited by 9
  4. 2024A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual Explanationscited by 5
  5. 2024The Effect of Data Poisoning on Counterfactual Explanationscited by 3
  6. 2026Fair Recourse for All: Ensuring Individual and Group Fairness in Counterfactual Explanationscited by 2
  7. 2024ProxiMix: Enhancing Fairness with Proximity Samples in Subgroupscited by 1
  8. 2024Towards Understanding the Influence of Training Samples on Explanationscited by 0
  9. 2026On the Hardness of Computing Counterfactual and Semifactual Explanations in XAIcited by 0